Hidden order: how adaptation builds complexity
Hidden order: how adaptation builds complexity
Efficient mechanisms for the supply of services in multi-agent environments
Decision Support Systems - Special issue on information and computational economics
Measuring computer performance: a practitioner's guide
Measuring computer performance: a practitioner's guide
Looking up data in P2P systems
Communications of the ACM
A Resource Management Architecture for Metacomputing Systems
IPPS/SPDP '98 Proceedings of the Workshop on Job Scheduling Strategies for Parallel Processing
Local Distributed Agent Matchmaking
CooplS '01 Proceedings of the 9th International Conference on Cooperative Information Systems
Adaptive Probabilistic Search for Peer-to-Peer Networks
P2P '03 Proceedings of the 3rd International Conference on Peer-to-Peer Computing
Commissioned Paper: Telephone Call Centers: Tutorial, Review, and Research Prospects
Manufacturing & Service Operations Management
A probabilistic approach to inference with limited information in sensor networks
Proceedings of the 3rd international symposium on Information processing in sensor networks
A survey of peer-to-peer content distribution technologies
ACM Computing Surveys (CSUR)
Search with Probabilistic Guarantees in Unstructured Peer-to-Peer Networks
P2P '05 Proceedings of the Fifth IEEE International Conference on Peer-to-Peer Computing
IEEE/ACM Transactions on Networking (TON) - Special issue on networking and information theory
Using Gossip for Dynamic Resource Discovery
ICPP '06 Proceedings of the 2006 International Conference on Parallel Processing
A survey on resource discovery mechanisms, peer-to-peer and service discovery frameworks
Computer Networks: The International Journal of Computer and Telecommunications Networking
Matchmaking for information agents
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
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Agent discovery and pairing is a core process for many multi-agent applications and enables the coordination of agents in order to contribute to the achievement of organisational-level objectives. Previous studies in peer-to-peer and sensor networks have shown the efficiency of probabilistic algorithms in object or resource discovery. In this paper we maintain confidence in such mechanisms and extend the work for the purpose of agent discovery for useful pairs that eventually coordinate to enhance their collective performance. The key difference in our mechanism is the use of domain-specific data that allows the discovery of relevant, useful agents while maintaining reduced communication costs. Agents employ a Bayesian inference model to control an otherwise random search, such that at each step a decision procedure determines whether it is worth searching further. In this way it attempts to capture something akin to the human disposition to give up after trying a certain number of alternatives and take the best offer seen. We benchmark the approach against exhaustive search (to establish an upper bound on costs), random and tabu--all of which it outperforms--and against an independent industrial standard simulator--which it also outperforms. We demonstrate using synthetic data--for the purpose of exploring the resilience of the approaches to extreme workloads--and empirical data, the effectiveness of a system that can identify "good enough" solutions to satisfy holistic organisational service level objectives.